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of Finland under the supervision of Academy Research Fellow Marcelo Hartmann and Research Fellow Luu Hoang Phuc Hau (Nanyang Technological University) . We have been developing computational algorithms and
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person will focus on either using and/or developing Vlasiator. Prior knowledge in at least one of the following areas is required: GPU technologies, high-performance computing, parallelisation algorithms
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developing computational algorithms and theory grounded in notions of information geometry and Riemannian geometry to enhance Bayesian statistical inference and machine-learning related methods. We are part of
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project is to develop a high-performance computing framework for mass spectrometry proteomics to enhance efficient processing and interpretation of large datasets using deep learning algorithms and GPU
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of computer science represented at the department (algorithms, networks, software engineering, AI, data science) Experience of working in highly interdisciplinary environments Experience in designing
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teaching merits and, if necessary, a teaching demonstration. Additional evaluation criteria for this position are: Experience in some area of computer science represented at the department (algorithms
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of Physics and Astronomy. AIPAD tackles the above questions by developing two innovative AI algorithms: The first algorithm will infer full SEP pitch-angle distributions (PADs) for spacecraft measurements
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embodied and justice-oriented approaches to datafication literacy, to support human agency and mitigate algorithmic harms. The focus will be on empirical research that uses different design and game-making
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metagenomics assembly” funded by the Research Council of Finland in the research group of University Lecturer Leena Salmela. We develop models, algorithms and data structures for high throughput sequencing data
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developed by the project partners will be based on two key technologies: machine learning algorithms that generate artificial yet realistic data points (synthetic health data) and secure multi-party